ML Paper Challenge Day 33 — Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding
3 min readMay 14, 2020
Day 33: 2020.05.14
Paper: Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding
Category: Model/Optimization
Deep Compression
Background:
- Having DNN locally: better privacy, less network bandwidth & real time processing -> But model size too large
- Large model -> High energy consumption
Goal:
- reduce the storage and energy required to run inference on such large networks so they can be deployed on mobile devices
